Ensemble-based deconvolution: when and why

David C. Henley, Carlos Montaña and Gary F. Margrave

ABSTRACT

Techniques for deconvolving seismic data often use the statistical properties of the
data themselves in designing operators to apply to the seismic traces. In the early stages
of seismic processing, individual seismic traces are usually members of one or more
ensembles like shot gathers or receiver gathers, and their statistical properties are related
not only to their own intrinsic character, but also to that of neighbouring traces within the
ensemble. We demonstrate that seismic traces contaminated primarily by bands of
coherent noise are often best deconvolved singly by a non-stationary algorithm like
Gabor deconvolution, but traces uniformly contaminated by varying levels of random
noise are better deconvolved by estimating an average operator for all the traces in an
ensemble.